Games scheduled/played on one calendar date (US-Eastern) for nba, mlb, or nhl; defaults to today. Returns the same envelope as query_table on the games table. NFL is week-based, not date-based — for nfl, use query_table on nfl/games with season_id + week filters instead. Requires an API key; rows count against quota.
| Name | Required | Description | Default |
|---|---|---|---|
| date | No | YYYY-MM-DD; defaults to today in US-Eastern time | |
| league | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does well: it discloses timezone handling, default-to-today behavior, response envelope reference (same as query_table), API key requirement, and quota consumption. It doesn't explicitly label the operation as read-only, but 'scheduled/played' and quota context imply a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with the primary purpose, followed by return format, then the key exclusion/alternative, and finally operational constraints. Every sentence adds necessary information without fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers all essential context for correct use: league scope, date semantics, timezone, default, NFL alternative, API key, and quota. Although there is no output schema, referencing the query_table envelope gives a clear pointer to the return format. No significant gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers date with pattern and default, but description adds US-Eastern timezone meaning and clarifies the league enum names explicitly ('nba, mlb, or nhl'). This adds value beyond the schema, especially for league which lacks a description in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns games for a specific calendar date across three leagues (nba, mlb, nhl) with a default to today. It distinguishes itself from siblings by explicitly noting the NFL exception and pointing to query_table as the alternative.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-use context (date-based leagues) and when-not-to-use (NFL is week-based, use query_table instead). Also mentions default behavior, timezone, API key requirement, and quota impact, giving clear operational guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct purpose: metadata discovery (list_leagues, list_tables, describe_table), data retrieval (query_table, get_record, graphql_query), convenience queries (games_on_date, game_markets, search_players), and account management (api_usage). No two tools have overlapping boundaries despite some sharing the ability to access data.
Most tools follow a clear verb_noun pattern (describe_table, get_record, list_leagues, list_tables, query_table, search_players), but a few use noun-only names (api_usage, game_markets, games_on_date, graphql_query). The naming is still readable and lowercase snake_case throughout, but the mix prevents a perfect score.
10 tools is well-scoped for a sports data API: it provides the essential discovery, schema inspection, querying, and retrieval operations, plus a few convenience wrappers. The count is neither too thin nor bloated.
The tool set fully covers the lifecycle of a read-only data API: exploring available leagues/tables, understanding table schemas, querying with filters and pagination, fetching by primary key, and accessing relational data via GraphQL. Convenience tools for games, markets, and player search address common use cases, and any data not directly exposed can be accessed through query_table.
